"The goal of the blog post is to give an **in-detail** explanation of **how** the transformer-based encoder-decoder architecture models *sequence-to-sequence* problems. We will focus on the ...

Service function chaining (SFC) provides the plat-form for flexible resource management by dynamically allocating resources to virtual and/or container network functions (VNFs/CNFs). To meet the ...

Transformer-based encoder-decoder models are the result of years of research on representation learning and model architectures. This notebook provides a short summary of the history of neural encoder ...

Text Auto-complete feature suggests a stream of words which complete a user's text as the user types each character. Such a feature is used in search engines, email programs, source code editors, ...

A typical image captioning model consists of two parts, an encoder/feature extractor and a decoder. A convolutional neural network (CNN) is often used as the encoder extracting fixed-size latent ...

Talking specifically about the deep learning models in time series, we see the huge success of the LSTM or RNN models because of their performance. In this article, we are going to discuss a model ...

Transformer Neural Networks Described Transformers are a type of machine learning model that specializes in processing and interpreting sequential data, making them optimal for natural language ...